R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

1 R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

1.1 R-project

https://cran.r-project.org/

1.2 RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

1.2.1 RStudio eklentileri

Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

# devtools::install_github("rstudio/addinexamples", type = "source")

1.3 X11

https://www.xquartz.org/

1.4 Java OS

https://support.apple.com/kb/dl1572

2 R zor şeyler için kolay, kolay şeyler için zor

R makes easy things hard, and hard things easy
Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf

3 R paketleri

3.1 Neden paketler var

I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

3.2 Paketleri nereden bulabiliriz

Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.html
Bioconductor
https://www.bioconductor.org
RecommendR
http://recommendr.info/
pkgsearch
CRAN package search
https://github.com/metacran/pkgsearch
Awesome R
https://awesome-r.com/

3.3 Kendi paket evrenini oluştur

pkgverse: Build a Meta-Package Universe
https://cran.r-project.org/web/packages/pkgverse/index.html

3.4 R için yardım bulma

# ?mean
# ??efetch
# help(merge)
# example(merge)

Vignette

RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow

https://stackoverflow.com/

Google uygun anahtar kelime

How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
— Emily Bovee (@ebovee09) August 10, 2018

Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/

Twitter

https://twitter.com/hashtag/rstats?src=hash

Reproducible Examples

Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX
— ZhiYang (@zhiiiyang) October 18, 2018

3.5 R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")

# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)

4 R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

5 RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

5.1 Excel

5.2 SPSS

5.3 csv

6 Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018

# library(nycflights13)
# summary(flights)

View(data)

data

head

tail

glimpse

str

skimr::skim()

7 Veriyi değiştirme

7.1 Veriyi kod ile değiştirelim

7.2 Veriyi eklentilerle değiştirme

7.3 RStudio aracılığıyla recode

questionr paketi kullanılacak

https://juba.github.io/questionr/articles/recoding_addins.html

8 Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)

# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

8.1 summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")

summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)

with(tobacco, print(ctable(smoker, diseased), method = 'render'))

with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))

summarytools::descr(iris, style = "rmarkdown")

descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")

# view(dfSummary(iris))

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")


# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")

# view(iris_stats_by_species)

data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')

what.is(iris)

freq(tobacco$gender, style = 'rmarkdown')

print(freq(tobacco$gender), method = 'render')

8.2 skimr

library(skimr)
skim(df)

8.3 DataExplorer

library(DataExplorer)
DataExplorer::create_report(df)

8.4 Grafikler

# library(ggplot2)
# library(mosaic)
# mPlot(irisdata)

ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')

print(ctable(tobacco$gender, tobacco$smoker), method = 'render')

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018

Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018

9 Rcmdr

library(Rcmdr)

Rcmdr::Commander()

A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

10 jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

11 Sonraki Konular

RStudio ile GitHub
Hipotez testleri
R Markdown ve R Notebook ile tekrarlanabilir rapor

12 Diğer kodlar

Diğer kodlar için bakınız: https://sbalci.github.io/

13 Geri Bildirim

Geri bildirim için tıklayınız: Geri bildirim formu

Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩

---
title: R ile analize başlarken^[Bu bir derlemedir, mümkün mertebe alıntılara referans
  vermeye çalıştım.]
author: "Derleyen [Serdar Balcı, MD, Pathologist](https://sbalci.github.io/)"
date: "`r format(Sys.Date())`"
output:
  rmdformats::html_clean:
    highlight: kate
  html_notebook:
    fig_caption: yes
    highlight: kate
    number_sections: yes
    theme: flatly
    toc: yes
    toc_depth: 5
    toc_float: yes
  pdf_document:
    toc: yes
    toc_depth: '5'
  html_document: 
    fig_caption: yes
    keep_md: yes
    toc: yes
    toc_depth: 5
    toc_float: yes
---



```{r knitr_init, echo=FALSE, cache=FALSE}
library(knitr)
library(rmdformats)

## Global options
options(max.print="75")
opts_chunk$set(echo=FALSE,
	             cache=TRUE,
               prompt=FALSE,
               tidy=TRUE,
               comment=NA,
               message=FALSE,
               warning=FALSE)
opts_knit$set(width=75)
```



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[![](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530113077/Image_2_vfy48b.png)](https://www.datacamp.com/community/tutorials/data-science-pitfalls)


- R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x


# R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

[![What is R?](http://img.youtube.com/vi/XcBLEVknqvY/0.jpg)](http://www.youtube.com/watch?v=XcBLEVknqvY)


## R-project

https://cran.r-project.org/

---

[![](https://ismayc.github.io/talks/ness-infer/img/engine.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#6)

---

## RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

---

[![](http://www-users.york.ac.uk/~er13/RStudio%20Anatomy.svg)](https://buzzrbeeline.blog/2018/07/04/rstudio-anatomy/)



---

### RStudio eklentileri

- Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/


```{r}
# devtools::install_github("rstudio/addinexamples", type = "source")
```


---

## X11

https://www.xquartz.org/

---

## Java OS

https://support.apple.com/kb/dl1572

---


# R zor şeyler için kolay, kolay şeyler için zor


- [R makes easy things hard, and hard things easy](http://r4stats.com/articles/why-r-is-hard-to-learn/)


- Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf



---


# R paketleri


## Neden paketler var

[![](https://ismayc.github.io/talks/ness-infer/img/appstore.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#7)

---

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I love the <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> community.<br>Someone is like, &quot;oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It&#39;s called pbplyr.&quot;<br>What a tribe.</p>&mdash; Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) <a href="https://twitter.com/Frankly_Data/status/1014189095294291968?ref_src=twsrc%5Etfw">July 3, 2018</a></blockquote>

---



https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

---

![](https://blog.mitchelloharawild.com/blog/2018-07-11-user-2018-feature-wall_files/final.jpg)

---

## Paketleri nereden bulabiliriz

- Available CRAN Packages By Name  
https://cran.r-project.org/web/packages/available_packages_by_name.html

- Bioconductor  
https://www.bioconductor.org

- RecommendR  
http://recommendr.info/

- pkgsearch  
CRAN package search  
https://github.com/metacran/pkgsearch

- Awesome R  
https://awesome-r.com/  


## Kendi paket evrenini oluştur

- pkgverse: Build a Meta-Package Universe  
https://cran.r-project.org/web/packages/pkgverse/index.html



---

## R için yardım bulma


```{r yardım}
# ?mean
# ??efetch
# help(merge)
# example(merge)
```



- Vignette

![](figures/vignette.png)

---

- RDocumentation
https://www.rdocumentation.org

- R Package Documentation
https://rdrr.io/

- GitHub

- Stackoverflow

https://stackoverflow.com/

- Google uygun anahtar kelime



<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">How I use <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <br>h/t <a href="https://twitter.com/ThePracticalDev?ref_src=twsrc%5Etfw">@ThePracticalDev</a> <a href="https://t.co/erRnTG0Ujr">pic.twitter.com/erRnTG0Ujr</a></p>&mdash; Emily Bovee (@ebovee09) <a href="https://twitter.com/ebovee09/status/1028037594947485696?ref_src=twsrc%5Etfw">August 10, 2018</a></blockquote>


---


![](figures/Google-package-name.png)

---



![](figures/Google-start-with-R.png)

---

- Awesome Cheatsheet
https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/


- Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/




- Twitter

https://twitter.com/hashtag/rstats?src=hash


- Reproducible Examples

<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Got a question to ask on <a href="https://twitter.com/SlackHQ?ref_src=twsrc%5Etfw">@SlackHQ</a> or post on <a href="https://twitter.com/github?ref_src=twsrc%5Etfw">@github</a>? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by <a href="https://twitter.com/JennyBryan?ref_src=twsrc%5Etfw">@JennyBryan</a>) <a href="https://twitter.com/hashtag/rstat?src=hash&amp;ref_src=twsrc%5Etfw">#rstat</a> <a href="https://t.co/gpuGXpFIsX">pic.twitter.com/gpuGXpFIsX</a></p>&mdash; ZhiYang (@zhiiiyang) <a href="https://twitter.com/zhiiiyang/status/1053006003711569920?ref_src=twsrc%5Etfw">October 18, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>





---

## R paket yükleme

```
install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")
```

```{r paket yükleme}
# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")
```


```{r paket cagirma, error=FALSE, message = FALSE, warning = FALSE, eval = TRUE, include = TRUE}
# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)
```

---

# R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

![](http://www.rstudio.com/images/docs/projects_new.png)

---

# RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

![](https://support.rstudio.com/hc/en-us/article_attachments/206277618/data-import-overview.gif)

---

## Excel

## SPSS

## csv


---

# Veriyi görüntüleme

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Spreadsheet users using <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>:  where&#39;s the data?<a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> users using spreadsheets:  where&#39;s the code?</p>&mdash; Leonard Kiefer (@lenkiefer) <a href="https://twitter.com/lenkiefer/status/1015587475580956672?ref_src=twsrc%5Etfw">July 7, 2018</a></blockquote>



```{r, results="markup"}
# library(nycflights13)
# summary(flights)
```



```
View(data)
```


```
data
```


```
head
```


```
tail
```


```
glimpse
```


```
str
```


```
skimr::skim()
```

---


# Veriyi değiştirme

## Veriyi kod ile değiştirelim

## Veriyi eklentilerle değiştirme

![](figures/change_data.png)


---


## RStudio aracılığıyla recode

*questionr* paketi kullanılacak

![](figures/level_recode.png)


---



https://juba.github.io/questionr/articles/recoding_addins.html


![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_1.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_2.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_3.png)


---

# Basit tanımlayıcı istatistikler

```
summary()
```

```
mean
```

```
median
```

```
min
```

```
max
```

```
sd
```

```
table()
```


```{r descriptive, echo=TRUE, include = TRUE}
library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)
```

---

```{r scatter, echo=TRUE, include=TRUE}
# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

```

## summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, print(ctable(smoker, diseased), method = 'render'))
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::descr(iris, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# view(dfSummary(iris))

```


![](figures/dfsummary.png)



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
dfSummary(tobacco, plain.ascii = FALSE, style = "grid")
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}

# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# view(iris_stats_by_species)
```

![](figures/DescriptiveStatistics.png)

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
what.is(iris)
```

---

```{r}
freq(tobacco$gender, style = 'rmarkdown')
```

---

```{r}
print(freq(tobacco$gender), method = 'render')
```

---

## skimr

```
library(skimr)
skim(df)
```

---

## DataExplorer

```
library(DataExplorer)
DataExplorer::create_report(df)
```


[![](https://static1.squarespace.com/static/58eef8846a4963e429687a4d/t/5bdfc2fb4d7a9c04ee50b7aa/1541391160702/dataExplorerGifLg.gif?format=1500w)](https://www.littlemissdata.com/blog/simple-eda)



---

## Grafikler

```{r}
# library(ggplot2)
# library(mosaic)
# mPlot(irisdata)
```

---

```{r}
ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')
```

---

```{r}
print(ctable(tobacco$gender, tobacco$smoker), method = 'render')
```

```
descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')
```


---



<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Here, building up a <a href="https://twitter.com/hashtag/ggplot2?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot2</a> as slowly as possible, <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>.  Incremental adjustments.  <a href="https://twitter.com/hashtag/rstatsteachingideas?src=hash&amp;ref_src=twsrc%5Etfw">#rstatsteachingideas</a> <a href="https://t.co/nUulQl8bPh">pic.twitter.com/nUulQl8bPh</a></p>&mdash; Gina Reynolds (@EvaMaeRey) <a href="https://twitter.com/EvaMaeRey/status/1029104656763572226?ref_src=twsrc%5Etfw">August 13, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


---


[![](https://raw.githubusercontent.com/dreamRs/esquisse/master/man/figures/esquisse.gif)](https://github.com/dreamRs/esquisse)


<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Dreaming of a fancy <a href="https://twitter.com/hashtag/Rstats?src=hash&amp;ref_src=twsrc%5Etfw">#Rstats</a> <a href="https://twitter.com/hashtag/ggplot?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot</a> <a href="https://twitter.com/hashtag/dataviz?src=hash&amp;ref_src=twsrc%5Etfw">#dataviz</a> but still scared of typing <a href="https://twitter.com/hashtag/code?src=hash&amp;ref_src=twsrc%5Etfw">#code</a>? <a href="https://twitter.com/_pvictorr?ref_src=twsrc%5Etfw">@_pvictorr</a> esquisse package has you covered <a href="https://t.co/1vIDXcVAAF">https://t.co/1vIDXcVAAF</a> <a href="https://t.co/RlTkptnrNv">pic.twitter.com/RlTkptnrNv</a></p>&mdash; Radoslaw Panczak (@RPanczak) <a href="https://twitter.com/RPanczak/status/1047019588658040832?ref_src=twsrc%5Etfw">October 2, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>










---

# Rcmdr

```
library(Rcmdr)

Rcmdr::Commander()

```


- A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/


---

# jamovi

https://www.jamovi.org/

![![](https://www.jamovi.org/assets/main-screenshot.png)](https://www.jamovi.org/)


https://blog.jamovi.org/2018/07/30/rj.html

![![](https://blog.jamovi.org/assets/images/rj.png)](https://blog.jamovi.org/2018/07/30/rj.html)

---

# Sonraki Konular

- RStudio ile GitHub
- Hipotez testleri
- R Markdown ve R Notebook ile tekrarlanabilir rapor


---

# Diğer kodlar

- Diğer kodlar için bakınız: [https://sbalci.github.io/](https://sbalci.github.io/)


---

# Geri Bildirim

- Geri bildirim için tıklayınız: _[Geri bildirim formu](https://goo.gl/forms/YjGZ5DHgtPlR1RnB3)_


---

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---







R ile analize başlarken¹

Derleyen Serdar Balcı, MD, Pathologist

2019-01-14

1 R yükleme

1.1 R-project

1.2 RStudio

1.2.1 RStudio eklentileri

1.3 X11

1.4 Java OS

2 R zor şeyler için kolay, kolay şeyler için zor

3 R paketleri

3.1 Neden paketler var

3.2 Paketleri nereden bulabiliriz

3.3 Kendi paket evrenini oluştur

3.4 R için yardım bulma

3.5 R paket yükleme

4 R studio ile proje oluşturma

5 RStudio ile veri yükleme

5.1 Excel

5.2 SPSS

5.3 csv

6 Veriyi görüntüleme

7 Veriyi değiştirme

7.1 Veriyi kod ile değiştirelim

7.2 Veriyi eklentilerle değiştirme

7.3 RStudio aracılığıyla recode

8 Basit tanımlayıcı istatistikler

8.1 summarytools

8.2 skimr

8.3 DataExplorer

8.4 Grafikler

9 Rcmdr

10 jamovi

11 Sonraki Konular

12 Diğer kodlar

13 Geri Bildirim

R ile analize başlarken1

Derleyen Serdar Balcı, MD, Pathologist

2019-01-14

1 R yükleme

1.1 R-project

1.2 RStudio

1.2.1 RStudio eklentileri

1.3 X11

1.4 Java OS

2 R zor şeyler için kolay, kolay şeyler için zor

3 R paketleri

3.1 Neden paketler var

3.2 Paketleri nereden bulabiliriz

3.3 Kendi paket evrenini oluştur

3.4 R için yardım bulma

3.5 R paket yükleme

4 R studio ile proje oluşturma

5 RStudio ile veri yükleme

5.1 Excel

5.2 SPSS

5.3 csv

6 Veriyi görüntüleme

7 Veriyi değiştirme

7.1 Veriyi kod ile değiştirelim

7.2 Veriyi eklentilerle değiştirme

7.3 RStudio aracılığıyla recode

8 Basit tanımlayıcı istatistikler

8.1 summarytools

8.2 skimr

8.3 DataExplorer

8.4 Grafikler

9 Rcmdr

10 jamovi

11 Sonraki Konular

12 Diğer kodlar

13 Geri Bildirim

R ile analize başlarken¹